AI-103 Computer Vision Practice Question
You are optimizing performance for a real-time video analytics pipeline that sends video frames to Azure AI Vision. Due to network bandwidth constraints, you need to reduce payload sizes while maintaining high OCR accuracy. What is the recommended approach?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Encode frames as optimized JPEG images with appropriate resolution scaling, ensuring text remains legible.
Compressing frames as high-quality JPEGs or WebP images and resizing them to fit optimal dimension thresholds without excessive downsampling preserves OCR accuracy while drastically reducing payload size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Encode frames as optimized JPEG images with appropriate resolution scaling, ensuring text remains legible.
Why this is correct
Optimized JPEG encoding and careful resolution scaling reduce payload bandwidth while preserving text fidelity.
- ✗
Convert frames to uncompressed 32-bit BMP format.
Why it's wrong here
BMP format is uncompressed and will drastically increase network bandwidth consumption.
- ✗
Extract audio tracks instead of video frames.
Why it's wrong here
Audio tracks cannot be processed by Azure AI Vision for OCR.
- ✗
Send raw YUV byte arrays directly without image containerization.
Why it's wrong here
Azure AI Vision REST API expects standard image formats like JPEG, PNG, or WebP.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-103 exam.